Warehouses and Lakes
Databases

How to load data from MySQL to BigQuery

Learn how to use Airbyte to synchronize your MySQL data into BigQuery within minutes.

TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

  1. set up MySQL as a source connector (using Auth, or usually an API key)
  2. set up BigQuery as a destination connector
  3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is MySQL

MySQL is an SQL (Structured Query Language)-based open-source database management system. An application with many uses, it offers a variety of products, from free MySQL downloads of the most recent iteration to support packages with full service support at the enterprise level. The MySQL server, while most often used as a web database, also supports e-commerce and data warehousing applications and more.

What is BigQuery

BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.

Prerequisites

  1. A MySQL account to transfer your customer data automatically from.
  2. A BigQuery account.
  3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including MySQL and BigQuery, for seamless data migration.

When using Airbyte to move data from MySQL to BigQuery, it extracts data from MySQL using the source connector, converts it into a format BigQuery can ingest using the provided schema, and then loads it into BigQuery via the destination connector. This allows businesses to leverage their MySQL data for advanced analytics and insights within BigQuery, simplifying the ETL process and saving significant time and resources.

Step 1: Set up MySQL as a source connector

1. Open the Airbyte UI and navigate to the "Sources" tab.

2. Click on the "Add Source" button and select "MySQL" from the list of available sources.

3. Enter a name for your MySQL source and click on the "Next" button.

4. Enter the necessary credentials for your MySQL database, including the host, port, username, and password.

5. Select the database you want to connect to from the drop-down menu.

6. Choose the tables you want to replicate data from by selecting them from the list.

7. Click on the "Test" button to ensure that the connection is successful.

8. If the test is successful, click on the "Create" button to save your MySQL source configuration.

9. You can now use your MySQL connector to replicate data from your MySQL database to your destination of choice.

Step 2: Set up BigQuery as a destination connector

1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.

2. Scroll down until you find the "BigQuery" destination connector and click on it.

3. Click the "Create Destination" button to begin setting up your BigQuery destination.

4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.

5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.

6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.

7. Finally, review your settings and click the "Create Destination" button to complete the setup process.

8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.

9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.

10. Follow the prompts to enter your source credentials and configure your sync settings.

11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.

12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.

Should you build or buy your data pipelines?

Download our free guide and discover the best approach for your needs, whether it's building your ELT solution in-house or opting for Airbyte Open Source or Airbyte Cloud.

Download now

Step 3: Set up a connection to sync your MySQL data to BigQuery

Once you've successfully connected MySQL as a data source and BigQuery as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select MySQL from the dropdown list of your configured sources.
  3. Select your destination: Choose BigQuery from the dropdown list of your configured destinations.
  4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
  5. Select the data to sync: Choose the specific MySQL objects you want to import data from towards BigQuery. You can sync all data or select specific tables and fields.
  6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from MySQL to BigQuery according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your BigQuery data warehouse is always up-to-date with your MySQL data.

Use Cases to transfer your MySQL data to BigQuery

Integrating data from MySQL to BigQuery provides several benefits. Here are a few use cases:

  1. Advanced Analytics: BigQuery’s powerful data processing capabilities enable you to perform complex queries and data analysis on your MySQL data, extracting insights that wouldn't be possible within MySQL alone.
  2. Data Consolidation: If you're using multiple other sources along with MySQL, syncing to BigQuery allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
  3. Historical Data Analysis: MySQL has limits on historical data. Syncing data to BigQuery allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: BigQuery provides robust data security features. Syncing MySQL data to BigQuery ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: BigQuery can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding MySQL data.
  6. Data Science and Machine Learning: By having MySQL data in BigQuery, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While MySQL provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to BigQuery, providing more advanced business intelligence options. If you have a MySQL table that needs to be converted to a BigQuery table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a MySQL account as an Airbyte data source connector.
  2. Configure BigQuery as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from MySQL to BigQuery after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Connectors Used

According to DB-engines Rankings, MySQL is the second most popular database. On the other hand, BigQuery is Google’s cost-effective, serverless, multi cloud enterprise data warehouse. Copying data from MySQL to BigQuery may be part of your overall data integration strategy, that offers a plethora of advantages, and leveraging one of the powerful data integration tools like Airbyte eases the process even more! Airbyte is like a data engineer's secret weapon! With its powerful capabilities, you can effortlessly connect MySQL to BigQuery that will provide you with the following benefits:

  1. Fast analytics without impacting operational workloads: BigQuery is designed for fast and efficient analytics. Furthermore, by creating a copy of your operational data, you can execute complex analytical queries without impacting your operational workloads.
  2. Creating a single source of truth and optimize reporting workflows: It can be time-consuming and challenging for analysts to work with multiple platforms. Combining data into a centralized data warehouse reduces this workload by serving as a single source of truth.
  3. Improved security: Replicating data out of MySQL into an analytical system such as BigQuery removes the need to grant permissions to data analysts on operational systems, which can improve security.
  4. Data insights that go deeper: MySQL is built for OLTP (Online Transaction Processing) operations well, while BigQuery handles OLAP (Online Analytical Processing). BigQuery is a serverless, cost-effective, multi-cloud data warehouse that can help you quickly turn big data into valuable insights.

This tutorial will show you how to use Airbyte cloud, which is a data integration platform, to set up a data replication pipeline from MySQL to BigQuery. You can also checkout another helpful article that takes you through the step-by-step process of connecting MySQL to Snowflake.

Let's get started!

Prerequisites

To follow the steps provided in this tutorial article you will make use of the following tools.

In this tutorial, you will set up the MySQL database on Microsoft Azure cloud. This is because the Airbyte cloud will make use of a public (not local) IP address to access MySQL. On the other hand, If you want to connect Airbyte to a local instance of MySQL, you may consider downloading and running Airbyte open-source.

Step 1: Set up MySQL as an Airbyte source

Create a MySQL server on the Azure cloud platform, and follow the Quickstart: Create an Azure Database for MySQL server by using the Azure portal.

ℹ️ We use the Azure cloud platform for this tutorial, but you can set up MySQL on any cloud platform you prefer.

Once the server set-up process is complete, open the server dashboard. On the side panel, click the connect button to open up the connection settings and copy the details. We will use them to connect with MySQL workbench, which will serve as the client from which we will build a test database with sample data.

Step 2: Connect MySQL workbench to MySQL server

Open your MySQL workbench and follow the steps to connect it to MySQL server, as instructed in: Quickstart: Use MySQL Workbench to connect and query data in Azure Database for MySQL.

If the above configuration is successful, your MySQL workbench is connected to your instance of MySQL. You can create and access some test data, which will be used to demonstrate Airbyte replication to BigQuery.

Step 3: Set up the MySQL database as a data source on Airbyte Cloud

Log in to your airbyte cloud account, click on the sources tab, and then the +new source button to create a new source.  On the Source type, select MySQL, then complete the details to match the settings you defined when you set up MySQL. This should look similar to the image shown below:

Step 4: Set up BigQuery

To set up BigQuery, access your account on the google developer console and create a new project. In your newly created project dashboard, click on the navigation menu on the right side and select “IAM & ADMIN” - Service Accounts.

Click on Create Service Account to create a service account. In the setup window, define your service account name, ID and click create to complete the setup process.

Setup the storage object admin role. On the Roles’ setup prompt that follows, search for cloud storage service and then select Storage Object Admin role. Click on done to finish the setup process.

Create a Cloud Storage bucket that will hold your data on BigQuery. Follow the steps below to create a storage bucket. On your project dashboard, search for Cloud Storage on the search bar and select it. Next, click the Create Bucket button to set up your storage bucket. Then provide the following information:

  1. Bucket Name. Enter a name for your bucket.
  2. Your data Location: The default option is US (multiple Regions in US).
  3. Your data storage class: Select the default standard option.
  4. Access control: Select the default Uniform option.
  5. Data Protection: Select the default none option.

Finally, click on create to finish the setup process. Follow the steps below to create an HMAC key access ID and Secret, which will be used later in Airbyte. After creating your storage bucket, navigate to the bucket dashboard and click on the settings option followed by Interoperability.

Click on create a key for a service account to select the service account initially created.

Finally, click on Create key to create your HMAC Access Key and Secret.

Copy the HMAC access key and secret. You will use it in the last step of setting up a connection between Airbyte cloud and BigQuery.

Step 5: Set up BigQuery as an airbyte destination

Log in to your Airbyte cloud account to set up the connection to the destination. Click Destinations and then click + New destination and select BigQuery as the destination to start the destination setup prompt. Configure the following details to set up the destination on airbyte:

  1. Destination type: BigQuery.
  2. Destination name: BigQuery.
  3. Project ID: The project ID you initially set when creating your project on the Google console.
  4. Dataset location: Select the location of your BigQuery dataset.
  5. Dataset Id: Your dataset ID.
  6. Loading method: Airbyte recommends GCS staging.
  7. Service Account Key JSON. Enter the Google Cloud Service Account Key in JSON format.
  8. Transformation Query Run Type: Set the default interactive to have BigQuery run interactive query jobs.
  9. Google BigQuery Client Chunk Size: Set the default value of 15 MiB.

This should look similar to the following:

Click on the setup connection button, at which point the connection to the BigQuery destination will be verified.  

Step 5: Sync data from MySQL to BigQuery using Airbyte Cloud

The last step in this process is establishing a connection between the source and destination. Click on Connections on the left tab, and then click on + New connection. Then select the MySQL source that you just created, followed by the new BigQuery destination. At this point you should see a screen similar to the following:

Give the new connection a name, and define the replication frequency. At the bottom of the above screen, you will see a list of the tables (not shown) that are available in your source, and can select the Sync mode for each one.  If you are not sure which Sync mode (a.k.a. replication mode) to choose, you may wish to read our blog on replication modes.

Once you start the first sync, your data from MySQL will be replicated to BigQuery

Log in to your Google developer console and navigate to your project to see the results of the first sync. In the image below, data has been replicated from a vehicles table into BigQuery – but the data you see will depend on your MySQL source data, and the tables that you have chosen to replicate.

Step 6: Wrapping up

In this step to step tutorial, you have learned how to:

  1. Configure a MySQL data source
  2. Configure BigQuery as a destination
  3. Create an Airbyte cloud connection that syncs data from MySQL to BigQuery

Airbyte’s data integration platform makes it easy to move data from across your enterprise into a single source of truth that is optimized for analytics! This has several benefits including: improved analytics, a single source of truth, improved security, and better data insights. If you have enjoyed this tutorial, you may check out Airbyte's tutorials and Airbyte’s blog to learn more about the platform. You can also join the conversation on our community Slack Channel, participate in discussions on Airbyte’s discourse, or sign up for our newsletter.

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Should you build or buy your data pipelines?

Download our free guide and discover the best approach for your needs, whether it's building your ELT solution in-house or opting for Airbyte Open Source or Airbyte Cloud.

Download now

Connectors Used

Frequently Asked Questions

What data can you extract from MySQL?

MySQL provides access to a wide range of data types, including:

1. Numeric data types: These include integers, decimals, and floating-point numbers.

2. String data types: These include character strings, binary strings, and text strings.

3. Date and time data types: These include date, time, datetime, and timestamp.

4. Boolean data types: These include true/false or yes/no values.

5. Spatial data types: These include points, lines, polygons, and other geometric shapes.

6. Large object data types: These include binary large objects (BLOBs) and character large objects (CLOBs).

7. Collection data types: These include arrays, sets, and maps.

8. User-defined data types: These are custom data types created by the user.

Overall, MySQL's API provides access to a wide range of data types, making it a versatile tool for managing and manipulating data in a variety of applications.

What data can you transfer to BigQuery?

You can transfer a wide variety of data to BigQuery. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from MySQL to BigQuery?

The most prominent ETL tools to transfer data from MySQL to BigQuery include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from MySQL and various sources (APIs, databases, and more), transforming it efficiently, and loading it into BigQuery and other databases, data warehouses and data lakes, enhancing data management capabilities.